P-06 The Transgenerational Effect of Substance Use Between Students, Parents & Grandparents

Using a survey conducted among college students at Andrews University, this study focused on substance use and sexual behaviors of students and the presence of familial substance use problems. Analysis indicated a significant association between substance use problems of fathers and their children’s substance use. There was a stronger statistical association between father’s substance use problems and male children’s alcohol use. In addition there was a significant relationship between grandparents substance use and youth sexual behavior for both genders. There is a need for further analysis of the study

The Transgenerational Effect of Substance Abuse

Using a survey conducted among college students at Andrews University, this study focused on substance use and sexual behaviors of students and the presence of familial substance use problems. Analysis indicated a significant association between substance use problems of fathers and their children\u27s substance use. There was a stronger statistical association between father\u27s substance use problems and male children\u27s alcohol use. In addition there was a significant relationship between grandparents substance use and youth sexual behavior for both genders. There is a need for further analysis of this study

Advances in generative models for dynamic scenes

Les réseaux de neurones sont un type de modèle d'apprentissage automatique (ML) qui résolvent des tâches complexes d'intelligence artificielle (AI) sans nécessiter de représentations de données élaborées manuellement. Bien qu'ils aient obtenu des résultats impressionnants dans des tâches nécessitant un traitement de la parole, d’image, et du langage, les réseaux de neurones ont encore de la difficulté à résoudre des tâches de compréhension de scènes dynamiques. De plus, l’entraînement de réseaux de neurones nécessite généralement de nombreuses données annotées manuellement, ce qui peut être un processus long et coûteux. Cette thèse est composée de quatre articles proposant des modèles génératifs pour des scènes dynamiques. La modélisation générative est un domaine du ML qui étudie comment apprendre les mécanismes par lesquels les données sont produites. La principale motivation derrière les modèles génératifs est de pouvoir, sans utiliser d’étiquettes, apprendre des représentations de données utiles; c’est un sous-produit de l'approximation du processus de génération de données. De plus, les modèles génératifs sont utiles pour un large éventail d'applications telles que la super-résolution d'images, la synthèse vocale ou le résumé de texte. Le premier article se concentre sur l'amélioration de la performance des précédents auto-encodeurs variationnels (VAE) pour la prédiction vidéo. Il s’agit d’une tâche qui consiste à générer les images futures d'une scène dynamique, compte tenu de certaines observations antérieures. Les VAE sont une famille de modèles à variables latentes qui peuvent être utilisés pour échantillonner des points de données. Comparés à d'autres modèles génératifs, les VAE sont faciles à entraîner et ont tendance à couvrir tous les modes des données, mais produisent souvent des résultats de moindre qualité. En prédiction vidéo, les VAE ont été les premiers modèles capables de produire des images futures plausibles à partir d’un contexte donné, un progrès marquant par rapport aux modèles précédents car, pour la plupart des scènes dynamiques, le futur n'est pas une fonction déterministe du passé. Cependant, les premiers VAE pour la prédiction vidéo produisaient des résultats avec des artefacts visuels visibles et ne fonctionnaient pas sur des ensembles de données réalistes complexes. Dans cet article, nous identifions certains des facteurs limitants de ces modèles, et nous proposons pour chacun d’eux une solution pour en atténuer l'impact. Grâce à ces modifications, nous montrons que les VAE pour la prédiction vidéo peuvent obtenir des résultats de qualité nettement supérieurs par rapport aux références précédentes, et qu'ils peuvent être utilisés pour modéliser des scènes de conduite autonome. Dans le deuxième article, nous proposons un nouveau modèle en cascade pour la génération vidéo basé sur les réseaux antagonistes génératifs (GAN). Après le succès des VAE pour prédiction vidéo, il a été démontré que les GAN produisaient des échantillons vidéo de meilleure qualité pour la génération vidéo conditionnelle à des classes. Cependant, les GAN nécessitent de très grandes tailles de lots ainsi que des modèles de grande capacité, ce qui rend l’entraînement des GAN pour la génération vidéo coûteux computationnellement, à la fois en termes de mémoire et en temps de calcul. Nous proposons de scinder le processus génératif en une cascade de sous-modèles, chacun d'eux résolvant un problème plus simple. Cette division nous permet de réduire considérablement le coût computationnel tout en conservant la qualité de l'échantillon, et nous démontrons que ce modèle peut s'adapter à de très grands ensembles de données ainsi qu’à des vidéos de haute résolution. Dans le troisième article, nous concevons un modèle basé sur le principe qu'une scène est composée de différents objets, mais que les transitions de trame (également appelées règles dynamiques) sont partagées entre les objets. Pour mettre en œuvre cette hypothèse de modélisation, nous concevons un modèle qui extrait d'abord les différentes entités d'une image. Ensuite, le modèle apprend à mettre à jour la représentation de l'objet d'une image à l'autre en choisissant parmi différentes transitions possibles qui sont toutes partagées entre les différents objets. Nous montrons que, lors de l'apprentissage d'un tel modèle, les règles de transition sont fondées sémantiquement, et peuvent être appliquées à des objets non vus lors de l'apprentissage. De plus, nous pouvons utiliser ce modèle pour prédire les observations multimodales futures d'une scène dynamique en choisissant différentes transitions. Dans le dernier article nous proposons un modèle génératif basé sur des techniques de rendu 3D qui permet de générer des scènes avec plusieurs objets. Nous concevons un mécanisme d'inférence pour apprendre les représentations qui peuvent être rendues avec notre modèle et nous optimisons simultanément ce mécanisme d'inférence et le moteur de rendu. Nous montrons que ce modèle possède une représentation interprétable dans laquelle des changements sémantiques appliqués à la représentation de la scène sont rendus dans la scène générée. De plus, nous montrons que, suite au processus d’entraînement, notre modèle apprend à segmenter les objets dans une scène sans annotations et que la représentation apprise peut être utilisée pour résoudre des tâches de compréhension de scène dynamique en déduisant la représentation de chaque observation.Neural networks are a type of Machine Learning (ML) models that solve complex Artificial Intelligence (AI) tasks without requiring handcrafted data representations. Although they have achieved impressive results in tasks requiring speech, image and language processing, neural networks still struggle to solve dynamic scene understanding tasks. Furthermore, training neural networks usually demands lots data that is annotated manually, which can be an expensive and time-consuming process. This thesis is comprised of four articles proposing generative models for dynamic scenes. Generative modelling is an area of ML that investigates how to learn the mechanisms by which data is produced. The main motivation for generative models is to learn useful data representations without labels as a by-product of approximating the data generation process. Furthermore, generative models are useful for a wide range of applications such as image super-resolution, voice synthesis or text summarization. The first article focuses on improving the performance of previous Variational AutoEncoders (VAEs) for video prediction, which is the task of generating future frames of a dynamic scene given some previous occurred observations. VAEs are a family of latent variable models that can be used to sample data points. Compared to other generative models, VAEs are easy to train and tend to cover all data modes, but often produce lower quality results. In video prediction VAEs were the first models that were able to produce multiple plausible future outcomes given a context, marking an advancement over previous models as for most dynamic scenes the future is not a deterministic function of the past. However, the first VAEs for video prediction produced results with visible visual artifacts and could not operate on complex realistic datasets. In this article we identify some of the limiting factors for these models, and for each of them we propose a solution to ease its impact. With our proposed modifications, we show that VAEs for video prediction can obtain significant higher quality results over previous baselines and that they can be used to model autonomous driving scenes. In the second article we propose a new cascaded model for video generation based on Generative Adversarial Networks (GANs). After the success of VAEs in video prediction, GANs were shown to produce higher quality video samples for class-conditional video generation. However, GANs require very large batch sizes and high capacity models, which makes training GANs for video generation computationally expensive, both in terms of memory and training time. We propose to split the generative process into a cascade of submodels, each of them solving a smaller generative problem. This split allows us to significantly reduce the computational requirements while retaining sample quality, and we show that this model can scale to very large datasets and video resolutions. In the third article we design a model based on the premise that a scene is comprised of different objects but that frame transitions (also known as dynamic rules) are shared among objects. To implement this modeling assumption we design a model that first extracts the different entities in a frame, and then learns to update the object representation from one frame to another by choosing among different possible transitions, all shared among objects. We show that, when learning such a model, the transition rules are semantically grounded and can be applied to objects not seen during training. Further, we can use this model for predicting multimodal future observations of a dynamic scene by choosing different transitions. In the last article we propose a generative model based on 3D rendering techniques that can generate scenes with multiple objects. We design an inference mechanism to learn representations that can be rendered with our model and we simultaneously optimize this inference mechanism and the renderer. We show that this model has an interpretable representation in which semantic changes to the scene representation are shown in the output. Furthermore, we show that, as a by product of the training process, our model learns to segment the objects in a scene without annotations and that the learned representation can be used to solve dynamic scene understanding tasks by inferring the representation of each observation

Impacts of Land Cover Change Caused by Urbanization on the Flood Regime of Msimbazi Catchment in Dar es Salaam, Tanzania

The hydrological processes of a catchment are the function of climate, land use and land cover. Changes in either climate or land use or land cover can result in alteration of the catchment’s hydrological processes. In the recent past, Msimbazi catchment in Dar es Salaam has undergone drastic land cover changes mainly due to urbanisation. These land cover changes caused changes in the behaviour of river flow resulting in frequent floods. Therefore, this study analyses the impacts of the changes in land cover due to urbanisation specifically with the changes in river flow, surface run-off and base-flows. Previously generated land cover maps of Msimbazi catchment and a combination of spatial and meteorological climate datasets were used to parameterise the hydrological model (SWAT). The model was calibrated and validated using the Sequential Uncertainty Fitting algorithm (SUFI-2) on a monthly resolution. The results show that there is an increase in surface run-off, mean river flow and the reduction of base-flow with the increase in urbanisation within the catchment. These increase in river flows, surface run-off and reduction of base-flow indicates the likelihood of an increase in flooding events in the catchment

Distributed Access Control with Blockchain

The specification and enforcement of network-wide policies in a single administrative domain is common in today's networks and considered as already resolved. However, this is not the case for multi-administrative domains, e.g. among different enterprises. In such situation, new problems arise that challenge classical solutions such as PKIs, which suffer from scalability and granularity concerns. In this paper, we present an extension to Group-Based Policy -- a widely used network policy language -- for the aforementioned scenario. To do so, we take advantage of a permissioned blockchain implementation (Hyperledger Fabric) to distribute access control policies in a secure and auditable manner, preserving at the same time the independence of each organization. Network administrators specify polices that are rendered into blockchain transactions. A LISP control plane (RFC 6830) allows routers performing the access control to query the blockchain for authorizations. We have implemented an end-to-end experimental prototype and evaluated it in terms of scalability and network latency.Comment: 7 pages, 9 figures, 2 table

Palaeodiets of Humans and Fauna at the Spanish Mesolithic Site of El Collado

The first human stable isotope results from the Spanish Levant, from the Mesolithic (ca. 7500 BP, Mesolithic IIIA phase) site of El Collado (near Oliva, Valencia) provide evidence for the consumption of marine protein by humans, estimated at approximately 25% of the dietary protein for some individuals. Isotopic analysis of human remains from other coastal Mesolithic sites in Europe, particularly along the Atlantic coast, also shows significant consumption of marine foods, but the amount of marine food consumed by the El Collado humans was much less than at those sites. This may be because of a different dietary adaptation or because the Mediterranean is much less productive than the Atlantic

INNOVATIONS ACTIVENESS IN THE SMES SECTOR IN BULGARIA AND SPAIN: EMPIRICAL STUDY

Innovations are one of the most influential factors on the economic growth and this is the reason why governments nowadays have significant concern about it. Researchers and academics from many countries study the innovations activeness and the type of innovations in various industry sectors but comparing the big scope of academic research on international level, there is insufficient research on the topic of innovativeness in Bulgaria. And as SMEs present a significant part of the regional and national economies including in Bulgaria, this empirical survey in Bulgaria and Spain is based on samples of SMEs from different sectors in both countries. In order to guarantee comparativeness the same questioning instrument was employed and the analysis revealed many similarities in the attitude to innovations and the type of innovations in small and medium sized companies in both countries. According to the survey results, the SMEs in Bulgaria focus more on innovations in the promotional policy egg. the marketing communications in contrast with the small and medium-sized companies in Spain where the stress in innovations is more on changes in distribution channels and in the pricing strategies. The comparative analysis with the Spanish companies points out that concerning the innovations in “design and packaging of goods” and “usage of new methods for goods and services promotion” the behaviour of the Bulgarian and Spanish companies is similar. At the end of the paper are drawn some conclusions about the innovations activeness of the SMEs in both countries and the similar problems.Innovations are one of the most influential factors on the economic growth and this is the reason why governments nowadays have significant concern about it. Researchers and academics from many countries study the innovations activeness and the type of innovations in various industry sectors but comparing the big scope of academic research on international level, there is insufficient research on the topic of innovativeness in Bulgaria. And as SMEs present a significant part of the regional and national economies including in Bulgaria, this empirical survey in Bulgaria and Spain is based on samples of SMEs from different sectors in both countries. In order to guarantee comparativeness the same questioning instrument was employed and the analysis revealed many similarities in the attitude to innovations and the type of innovations in small and medium sized companies in both countries. According to the survey results, the SMEs in Bulgaria focus more on innovations in the promotional policy egg. the marketing communications in contrast with the small and medium-sized companies in Spain where the stress in innovations is more on changes in distribution channels and in the pricing strategies. The comparative analysis with the Spanish companies points out that concerning the innovations in “design and packaging of goods” and “usage of new methods for goods and services promotion” the behaviour of the Bulgarian and Spanish companies is similar. At the end of the paper are drawn some conclusions about the innovations activeness of the SMEs in both countries and the similar problems

A developmental state in Africa: what can policy makers in Africa learn from the idea of developmental state ?

The developmental state draws interest as it provides an alternative methodology of achieving development. Its basis is the success experienced by certain East Asian states in achieving development in a short space of time. Therefore the aim of this research is to ascertain whether African policy makers can draw lessons from the experience which has been encapsulated in developmental state theory. The advent of development in Africa will be discussed with special focus placed on South Africa’s economic and political landscap